IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v8y2021i3p1-13.html
   My bibliography  Save this article

A New Approach for Deception Detection in Open Domain Text

Author

Listed:
  • Jamil R. Alzghoul

    (Albalqa Applied University, Jordan)

  • Muath Alzghool

    (Seneca College, Canada)

  • Emad E. Abdallah

    (Hashemite University, Jordan)

Abstract

The gigantic growth of platforms that give individuals the ability to write a review that is visible to everyone and the huge number of documents shared on the internet have triggered the researchers to try to detect if these platforms are trying to mislead and deceive people. There is a crucial need to find ways to automatically identify fake reviews and detect deceptive people or groups. The main aim of this research is to detect deception in open domain text by using a machine learning technique. Several sets of features are used to analyse the text including unigram, part of speech, and production rules. The experimental results showed that combined feature sets of (part of speech and production rules) using the support vector machine classifier achieve the best accuracy, and it clearly improves on the accuracy of the results reported in a previous study.

Suggested Citation

  • Jamil R. Alzghoul & Muath Alzghool & Emad E. Abdallah, 2021. "A New Approach for Deception Detection in Open Domain Text," International Journal of Business Analytics (IJBAN), IGI Global, vol. 8(3), pages 1-13, July.
  • Handle: RePEc:igg:jban00:v:8:y:2021:i:3:p:1-13
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2021070101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:8:y:2021:i:3:p:1-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.